Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 26 for “"Tensor Decomposition"”.
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Tensor decomposition and parallelization of Markov Decision Processes
… paper introduced an MDP representation as a tensor composition of a set of smaller component MDPs, and suggested a method for solving an MDP by decomposition into its tensor components and solving the smaller problems in parallel, combining their solutions into one for the original problem. …
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Agile quadrotor maneuvering using tensor-decomposition-based globally optimal control and onboard visual-inertial estimation
… resulting optimization problem is solved using tensor-train-decomposition-based compressed continuous computation techniques. The platform's capabilities and the potential of these types of controllers are demonstrated in both simulation studies and in experiments.
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Learning Algorithms for Mixtures of Linear Dynamical Systems: A Practical Approach
… a recent polynomial-time algorithm based on a tensor decomposition with learning guarantees in a general setting, with some simplifications and minor optimizations. Our largest contribution is giving the first expectation-maximization (E-M) algorithm for learning a mixture of LDS’s, and an …
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Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents
… transition dynamics of the agents as a low rank tensor decomposition of latent factors associated with agents, states, and actions. We perform experiments on various benchmark environments and demonstrate improvement over existing offline approaches in the scarce data regime.
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Algorithms and software for efficient tensor decompositions
Tensors, which generalize vectors and matrices to higher dimensions, provide a powerful framework for representing and analyzing multi-way data arising in science and engineering. Their ability to capture complex, multi-relational structures makes them invaluable in applications ranging from …
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Secant varieties of Spinor varieties and of other generalized Grassmannians
… varieties are among the main protagonists in tensor decomposition, whose study involves both pure and applied mathematic areas. Despite they have been studied for decades, several aspects of their geometry are still mysterious, among which identifiability and singularity of their points. In …
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New progress in hot-spots detection, partial-differential-equation-based model identification and statistical computation
… a statistical method to under the framework of tensor decomposition and our method has three steps. First, we fit the observed data into a Smooth Sparse Decomposition Tensor (SSD-Tensor) model that serves as a dimension reduction and de-noising technique: it is an additive model that decomposes …
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Identifying drug-target and drug-disease associations using computational intelligence
… In this step, we develop NTD-DR, a nonnegative tensor decomposition approach where multiple similarities for drugs, targets, and diseases are used to identify the associations between drugs and diseases to be used for drug repositioning. The detail of each method is discussed in Chapters 3, 4, …
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Understanding Deep Learning via Analyzing Training Dynamics
… analysis techniques of training dynamics in tensor decompositions and then showcase the explanation of two phenomena by analyzing gradient descent dynamics. </p><p>In the first part, we analyze the gradient descent dynamics in over-parameterized tensor decompositions. For non-orthogonal …
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Mining Complex High-Order Datasets
… and built on well-studied mathematical tools, tensor analysis methodologies have only recently entered widespread use in the data mining community and remain relatively absent from the literature within the biomedical domain. Furthermore, naive tensor approaches suffer from fundamental …
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Engineering-driven Machine Learning Methods for System Intelligence
… can be summarized in three aspects. First, tensor decomposition is incorporated to approximately compress the convolutional (Conv) layer in Deep Neural Network (DNN), and a novel layer is proposed accordingly. Compared with the Conv layer, the proposed layer significantly reduces the number …
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Bayesian Tensor Modeling of High-Dimensional Neuroimaging Data
… emission tomography (PET), and diffusion tensor imaging (DTI) generate high-dimensional data that can be used to identify biomarkers indicative of disease progression. While these data offer valuable opportunities for early diagnosis and personalized treatment, their sheer dimensionality …
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An Iterative Method for Canonical Polyadic Decomposition of Tensors
<p>The Singular Value Decomposition (SVD) of matrices is widely used in least-squares regression, image and data processing, principal component analysis and many other applications. Often only the larger singular values of the matrix are needed to reconstruct the matrix and those below a given …
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Spatial correlation tensor and query-augmented active clustering
… the pixel correlations. Multi-dimensional tensor data has gained increasing attention recently, especially in biomedical imaging analyses. However, most existing tensor models are only based on the mean information of imaging pixels. Motivated by multimodal optical imaging data in a breast …
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Statistical learning approaches for obtaining interpretable reduced representations of multimodal sequencing datasets
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms
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A multiresolution approach for tensor completion from coarse and partial observations
Existing tensor completion formulation mostly relies on partial observations from a single tensor. However, tensors extracted from real-world data often are more complex due to: (i) Partial observation: Only a small subset of tensor elements are available. (ii) Coarse observation: Some tensor modes …
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Advanced Data Analytics for Quality Assurance of Smart Additive Manufacturing
… image stream data using smooth and sparse tensor completion is proposed and applied to data acquisition of additive manufacturing. The qualified thermal data is able to extract useful information like boundary velocity, thermal gradient, etc. 2. To effectively extract features for high …
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Individualized learning and integration for multi-modality data
… variable selection and multi-modality tensor learning with an application in medical imaging analysis. In the first part of the thesis, we develop a model-based subgrouping method for longitudinal data. Specifically, we propose an unbiased estimating equation approach for a …
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Two New Applications of Tensors to Machine Learning for Wireless Communications
… ML algorithms has obviously been a challenge. Tensors provide a useful framework to represent multi-dimensional data in an integrated manner by preserving relationships in data across different dimensions. This thesis studies two new applications of tensors to ML for wireless communications …
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Geophysical signatures of crack initiation and growth in rocks under uniaxial compression
… using various approaches based on the moment tensor decomposition components and the polarity of the AE signals. Tensile cracking was identified as the dominant mode of deformation during brittle creep experiments regardless of the applied stress. These observations are very useful to …
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